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A molecular approach integrating genomic and DNA methylation profiling for tissue of origin identification in lung-specific cancer of unknown primary

BACKGROUND: Determining the tissue of origin (TOO) is essential for managing cancer of unknown primary (CUP). In this study, we evaluated the concordance between genome profiling and DNA methylation analysis in determining TOO for lung-specific CUP and assessed their performance by comparing the cli...

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Autores principales: Chen, Kaiyan, Zhang, Fanrong, Yu, Xiaoqing, Huang, Zhiyu, Gong, Lei, Xu, Yanjun, Li, Hui, Yu, Sizhe, Fan, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8981640/
https://www.ncbi.nlm.nih.gov/pubmed/35382836
http://dx.doi.org/10.1186/s12967-022-03362-2
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author Chen, Kaiyan
Zhang, Fanrong
Yu, Xiaoqing
Huang, Zhiyu
Gong, Lei
Xu, Yanjun
Li, Hui
Yu, Sizhe
Fan, Yun
author_facet Chen, Kaiyan
Zhang, Fanrong
Yu, Xiaoqing
Huang, Zhiyu
Gong, Lei
Xu, Yanjun
Li, Hui
Yu, Sizhe
Fan, Yun
author_sort Chen, Kaiyan
collection PubMed
description BACKGROUND: Determining the tissue of origin (TOO) is essential for managing cancer of unknown primary (CUP). In this study, we evaluated the concordance between genome profiling and DNA methylation analysis in determining TOO for lung-specific CUP and assessed their performance by comparing the clinical responses and survival outcomes of patients predicted with multiple primary or with metastatic cancer. METHODS: We started by retrospectively screening for CUP patients who presented with both intra- and extrathoracic tumors. Tumor samples from included patients were analyzed with targeted sequencing with a 520-gene panel and targeted bisulfite sequencing. TOO inferences were made in parallel via an algorithm using genome profiles and time interval between tumors and via machine learning-based classification of DNA methylation profiles. RESULTS: Four hundred patients were screened retrospectively. Excluding patients definitively diagnosed with conventional diagnostic work-up or without available samples, 16 CUP patients were included. Both molecular approaches alone enabled inference of clonality for all analyzed patients. Genome profile enabled TOO inference for 43.8% (7/16) patients, and the percentage rose to 68.8% (11/16) after considering inter-tumor time lag. On the other hand, DNA methylation analysis was conclusive for TOO prediction for 100% (14/14) patients with available samples. The two approaches gave 100% (9/9) concordant inferences regarding clonality and TOO identity. Moreover, patients predicted with metastatic disease showed significantly shorter overall survival than those with multiple primary tumors. CONCLUSIONS: Genome and DNA methylation profiling have shown promise as individual analysis for TOO identification. This study demonstrated the feasibility of incorporating the two methods and proposes an integrative scheme to facilitate diagnosing and treating lung-specific CUPs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03362-2.
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spelling pubmed-89816402022-04-06 A molecular approach integrating genomic and DNA methylation profiling for tissue of origin identification in lung-specific cancer of unknown primary Chen, Kaiyan Zhang, Fanrong Yu, Xiaoqing Huang, Zhiyu Gong, Lei Xu, Yanjun Li, Hui Yu, Sizhe Fan, Yun J Transl Med Research BACKGROUND: Determining the tissue of origin (TOO) is essential for managing cancer of unknown primary (CUP). In this study, we evaluated the concordance between genome profiling and DNA methylation analysis in determining TOO for lung-specific CUP and assessed their performance by comparing the clinical responses and survival outcomes of patients predicted with multiple primary or with metastatic cancer. METHODS: We started by retrospectively screening for CUP patients who presented with both intra- and extrathoracic tumors. Tumor samples from included patients were analyzed with targeted sequencing with a 520-gene panel and targeted bisulfite sequencing. TOO inferences were made in parallel via an algorithm using genome profiles and time interval between tumors and via machine learning-based classification of DNA methylation profiles. RESULTS: Four hundred patients were screened retrospectively. Excluding patients definitively diagnosed with conventional diagnostic work-up or without available samples, 16 CUP patients were included. Both molecular approaches alone enabled inference of clonality for all analyzed patients. Genome profile enabled TOO inference for 43.8% (7/16) patients, and the percentage rose to 68.8% (11/16) after considering inter-tumor time lag. On the other hand, DNA methylation analysis was conclusive for TOO prediction for 100% (14/14) patients with available samples. The two approaches gave 100% (9/9) concordant inferences regarding clonality and TOO identity. Moreover, patients predicted with metastatic disease showed significantly shorter overall survival than those with multiple primary tumors. CONCLUSIONS: Genome and DNA methylation profiling have shown promise as individual analysis for TOO identification. This study demonstrated the feasibility of incorporating the two methods and proposes an integrative scheme to facilitate diagnosing and treating lung-specific CUPs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03362-2. BioMed Central 2022-04-05 /pmc/articles/PMC8981640/ /pubmed/35382836 http://dx.doi.org/10.1186/s12967-022-03362-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Chen, Kaiyan
Zhang, Fanrong
Yu, Xiaoqing
Huang, Zhiyu
Gong, Lei
Xu, Yanjun
Li, Hui
Yu, Sizhe
Fan, Yun
A molecular approach integrating genomic and DNA methylation profiling for tissue of origin identification in lung-specific cancer of unknown primary
title A molecular approach integrating genomic and DNA methylation profiling for tissue of origin identification in lung-specific cancer of unknown primary
title_full A molecular approach integrating genomic and DNA methylation profiling for tissue of origin identification in lung-specific cancer of unknown primary
title_fullStr A molecular approach integrating genomic and DNA methylation profiling for tissue of origin identification in lung-specific cancer of unknown primary
title_full_unstemmed A molecular approach integrating genomic and DNA methylation profiling for tissue of origin identification in lung-specific cancer of unknown primary
title_short A molecular approach integrating genomic and DNA methylation profiling for tissue of origin identification in lung-specific cancer of unknown primary
title_sort molecular approach integrating genomic and dna methylation profiling for tissue of origin identification in lung-specific cancer of unknown primary
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8981640/
https://www.ncbi.nlm.nih.gov/pubmed/35382836
http://dx.doi.org/10.1186/s12967-022-03362-2
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